Goal
Automatically recognise, process and evaluate structured text data. Tool builds up an integrity score (0-100 score) for a professional advisor based on the attributes of the reviews they receive.
Role
I developed a python tool to recognise, process and evaluate structured text data.
Capability
Python tool reads input text data line by line and calculates an integrity score based on a number of factors:
- Lots to say: Genuine reviewers tend to say less – knock 0.5% points off for each review that contains more than 100 words.
- Burst: If a number of reviews come in within the same time frame – knock 40% points off if 2 or more come through in the same minute, 20% points if they come through in the same hour.
- Same Device: We have a system that forms a readable tag (e.g. LB4-6WR) based on the browser/device/location. If we are seeing multiple reviews coming from the same device knock 30% points off each time.
- All-Star: Non-genuine reviews are likely to have a five-star rating take 2% points off the integrity score for each review that has 5 stars; quadruple the penalty if the average is under 3.5 stars.
- Solicited: If the review was left by someone who was invited by the professional then add 3% points to the integrity score.
Tech Stack
Python, API development
Year
2020